MHASSNet: A Deep Neural Network-based Automatic Sleep Staging Model Using Hybrid Attention Mechanism and State Space Model

Automatic sleep stage classification is an important means of measuring sleep quality. This article introduces a new deep learning architecture, MHASSNet, which aims to improve the accuracy of sleep stage classification to more effectively assess sleep quality. The model features a hybrid attention mechanism and multimodal signal processing capability. First, MHASSNet extracts low-frequency and high-frequency features through a multi-scale convolutional neural network MSCNN . Then, it utilizes a hybrid attention module MAM that combines spatial and channel attention mechanisms to capture the important spatiotemporal dependencies between these features. Additionally, a state-space model SSM is employed to enhance the understanding of temporal context information. Experimental results show that, when tested on two public datasets, MHASSNet achieved significant results across various evaluation metrics, demonstrating its superior performance and potential applications in automatic sleep stage classification.